R Notebook

packages = c('rgdal', 'maptools', 'raster','spatstat', 'tmap', 'sf', 'tidyverse', 'SpatialAcc') 
for (p in packages){ 
  if(!require(p, character.only = T)){ 
    install.packages(p) } 
  library(p,character.only = T) }

Singapore Planning Subzone (MP14_SUBZONE_WEB_PL)

mpsz = st_read(dsn = "data", layer = "MP14_SUBZONE_WEB_PL")

Singapore Residents by Subzone, Age Group and Sex (Gender)

population = read_csv("data/respopagsex2000to2017.csv")

Singapore Residents by Subzone, Age Group and Sex, June 2017 (Gender)

popage2017pre <- population %>%
  filter(Time == 2017) %>%
  filter(AG == "65_to_69" | AG == "70_to_74" | AG == "75_to_79" | AG == "80_to_84" | AG == "85_and_over") %>%
  group_by(SZ)

popage2017pre_added <- aggregate(Pop ~ SZ, popage2017pre, sum)

Total Number of Clinics in Singapore

clinics = read_csv("data/clinics_hcidirectory.csv")

clinics_essentials <- c("clinic_name", "address", "LAT", "LONG", "X", "Y") # remove postal code

clinics <- clinics[clinics_essentials]

Total Number of TCM Clinics in Singapore

tcm = read_csv("data/tcm_tcmboard.csv")

tcm <- tcm[!duplicated(tcm$tcm_place_name), ]
  
tcm_essentials <- c("tcm_place_name", "tcm_address", "LAT", "LONG", "X", "Y")

tcm <- tcm[tcm_essentials]

names(tcm)[names(tcm) == "tcm_place_name"] <- "clinic_name"
names(tcm)[names(tcm) == "tcm_address"] <- "address"

Combine TCM and clinics

clinics_combined <- rbind(clinics, tcm)

Number of HDB blocks per planning subzone

HDB = read_csv("data/hdb_property_information.csv")

HDB_sf <- st_as_sf(HDB, coords = c("X", "Y"), crs = st_crs(mpsz))

Residents by Age Group & Type of Dwelling, Annual

popByDwelling = read_csv("data/residents-by-age-group-and-type-of-dwelling-detailed-categories-annual.csv")

Residents by Age Group & Type of Dwelling, Annual 2017

popByDwelling2017 <- popByDwelling %>%
  filter(year == 2017) %>%
  filter(level_1 == "65-69 Years" 
         | level_1 == "85 Years & Over" 
         | level_1 == "70-74 Years" 
         | level_1 == "75-79 Years"  
         | level_1 == "80-84 Years") %>%
  group_by(level_3)

popByDwelling2017
popByDwelling2017_added <- aggregate(value ~ level_3, popByDwelling2017, sum)

No. of blocks per subzone

mpsz_HDB <- st_join(HDB_sf,mpsz)

mpsz_HDB$`1_2room_total` <- mpsz_HDB$`1room_sold` + mpsz_HDB$`2room_sold` + mpsz_HDB$`1room_rental` + mpsz_HDB$`2room_rental`

mpsz_HDB_1_2_room = aggregate(mpsz_HDB$`1_2room_total`, by=list(SUBZONE=mpsz_HDB$SUBZONE_N), FUN=sum) %>%
  rename('No_of_1_2_room' = 'x') 

mpsz_HDB$`3room_total` <- mpsz_HDB$`3room_sold` + mpsz_HDB$`3room_rental` + mpsz_HDB$`other_room_rental`

mpsz_HDB_3_room_added = aggregate(mpsz_HDB$`3room_total`, by=list(SUBZONE=mpsz_HDB$SUBZONE_N), FUN=sum) %>%
  rename('No_of_3_room' = 'x') 

mpsz_HDB$`4room_total` <- mpsz_HDB$`4room_sold`

mpsz_HDB_4_room_added = aggregate(mpsz_HDB$`4room_total`, by=list(SUBZONE=mpsz_HDB$SUBZONE_N), FUN=sum) %>%
  rename('No_of_4_room' = 'x') 

mpsz_HDB$`5room_exec_total` <- mpsz_HDB$`5room_sold` + mpsz_HDB$`exec_sold`

mpsz_HDB_5_room_exec_added = aggregate(mpsz_HDB$`5room_exec_total`, by=list(SUBZONE=mpsz_HDB$SUBZONE_N), FUN=sum) %>%
  rename('No_of_5_room_exec' = 'x')   

mpsz_HDB_added <- left_join(mpsz_HDB_1_2_room, mpsz_HDB_3_room_added, by ='SUBZONE') %>%
  left_join(., mpsz_HDB_4_room_added, by = 'SUBZONE') %>%
  left_join(., mpsz_HDB_5_room_exec_added, by = 'SUBZONE')

mpsz_HDB_added$`total_units` <- mpsz_HDB_added$`No_of_1_2_room` + mpsz_HDB_added$`No_of_3_room` + mpsz_HDB_added$`No_of_4_room` + mpsz_HDB_added$`No_of_5_room`

total_1_2_room_units <- sum(mpsz_HDB_added$No_of_1_2_room) 
total_3_room_units <- sum(mpsz_HDB_added$No_of_3_room) 
total_4_room_units <- sum(mpsz_HDB_added$No_of_4_room) 
total_5_room_exec_units <- sum(mpsz_HDB_added$No_of_5_room_exec) 

No. of elderly per block in every subzone

total_1_2_room_units <- sum(mpsz_HDB_added$No_of_1_2_room) 
total_3_room_units <- sum(mpsz_HDB_added$No_of_3_room) 
total_4_room_units <- sum(mpsz_HDB_added$No_of_4_room) 
total_5_room_units <- sum(mpsz_HDB_added$No_of_5_room) 

mpsz_HDB$No_of_Elderly_in_block_1_2 <- ifelse(mpsz_HDB$`1_2room_total` == 0, 0, mpsz_HDB$`1_2room_total`/total_1_2_room_units) * popByDwelling2017_added$value[popByDwelling2017_added$level_3=="HDB 1- And 2-Room Flats"]

mpsz_HDB$No_of_Elderly_in_block_3 <- ifelse(mpsz_HDB$`3room_total` == 0, 0, mpsz_HDB$`3room_total`/total_3_room_units) * popByDwelling2017_added$value[popByDwelling2017_added$level_3=="HDB 3-Room Flats"] 

mpsz_HDB$No_of_Elderly_in_block_4 <- ifelse(mpsz_HDB$`4room_total` == 0, 0, mpsz_HDB$`4room_total`/total_4_room_units) * popByDwelling2017_added$value[popByDwelling2017_added$level_3=="HDB 4-Room Flats"]

mpsz_HDB$No_of_Elderly_in_block_5 <- ifelse(mpsz_HDB$`5room_exec_total` == 0, 0, mpsz_HDB$`5room_exec_total`/total_5_room_exec_units) * popByDwelling2017_added$value[popByDwelling2017_added$level_3=="HDB 5-Room And Executive Flats"]
                                    

mpsz_HDB$No_of_Elderly_in_block <- mpsz_HDB$No_of_Elderly_in_block_1_2 + mpsz_HDB$No_of_Elderly_in_block_3 + mpsz_HDB$No_of_Elderly_in_block_4 + mpsz_HDB$No_of_Elderly_in_block_5

mpsz_HDB$No_of_Elderly_in_block <- round(mpsz_HDB$No_of_Elderly_in_block)

clinics_combined <- st_as_sf(clinics_combined, coords = c("X", "Y"))
class(clinics_combined)

[1] "sf"         "tbl_df"     "tbl"        "data.frame"

#assign capacity of 2 per clinic
clinics_combined['capacity'] <- 2

mpsz_HDB_coords <- mpsz_HDB %>% st_coordinates() 
clinics_combined_coords <- clinics_combined %>% st_coordinates()

dm <- distance(mpsz_HDB_coords, clinics_combined_coords)
acc_hansen <- data.frame(ac(mpsz_HDB$No_of_Elderly_in_block, clinics_combined$capacity, dm, power=0.01, family="Hansen"))
colnames(acc_hansen) <- "accHansen"
acc_hansen <- tbl_df(acc_hansen)
HDB_acc <- bind_cols(mpsz_HDB, acc_hansen)

tmap_mode("view")

tm_shape(mpsz_HDB)+
  tm_symbols(size = 0.1)+
tm_shape(HDB_acc)+
tm_bubbles(col = "accHansen",
           n=5,
           style = "quantile",
           size = 0.1,
           border.col = "black",
           border.lwd = 1)